Python?seaborn?barplot畫圖案例

目錄

• 默認barplot
• 使用案例
• 修改capsize
• 顯示error bar得值
• annotata error bar
• error bar選取sd
• 設置置信區間(68)
• 設置置信區間(95)
• dataframe aggregate函數使用
• dataframe aggregate 自定義函數
• dataframe aggregate 自定義函數2
• seaborn顯示網格
• seaborn設置刻度
• 使用其他estaimator

默認barplot

import seaborn as snsimport matplotlib.pyplot as plt import numpy as np sns.set_theme(style="whitegrid")df = sns.load_dataset("tips")#默認畫條形圖sns.barplot(x="day",y="total_bill",data=df)plt.show()#計算平均值看是否和條形圖得高度一致print(df.groupby("day").agg({"total_bill":[np.mean]}))print(df.groupby("day").agg({"total_bill":[np.std]}))# 注意這個地方error bar顯示并不是標準差

     total_bill           meandayThur  17.682742Fri   17.151579Sat   20.441379Sun   21.410000     total_bill            stddayThur   7.886170Fri    8.302660Sat    9.480419Sun    8.832122

使用案例

# import librariesimport seaborn as snsimport numpy as npimport matplotlib.pyplot as plt# load datasettips = sns.load_dataset("tips")# Set the figure sizeplt.figure(figsize=(14, 8))# plot a bar chartax = sns.barplot(x="day", y="total_bill", data=tips, estimator=np.mean, ci=85, capsize=.2, color='lightblue')

修改capsize

ax=sns.barplot(x="day",y="total_bill",data=df,capsize=1.0)plt.show()

顯示error bar得值

import seaborn as snsimport matplotlib.pyplot as plt sns.set_theme(style="whitegrid")df = sns.load_dataset("tips")#默認畫條形圖ax=sns.barplot(x="day",y="total_bill",data=df)plt.show()for p in ax.lines:    width = p.get_linewidth()    xy = p.get_xydata() # 顯示error bar得值    print(xy)    print(width)    print(p)

[[ 0.         15.85041935] [ 0.         19.64465726]]2.7Line2D(_line0)[[ 1.         13.93096053] [ 1.         21.38463158]]2.7Line2D(_line1)[[ 2.         18.57236207] [ 2.         22.40351437]]2.7Line2D(_line2)[[ 3.         19.66244737] [ 3.         23.50109868]]2.7Line2D(_line3)

annotata error bar

fig, ax = plt.subplots(figsize=(8, 6))sns.barplot(x='day', y='total_bill', data=df, capsize=0.2, ax=ax)# show the meanfor p in ax.patches:    h, w, x = p.get_height(), p.get_width(), p.get_x()    xy = (x + w / 2., h / 2)    text = f'Mean:n{h:0.2f}'    ax.annotate(text=text, xy=xy, ha='center', va='center')ax.set(xlabel='day', ylabel='total_bill')plt.show()

error bar選取sd

import seaborn as snsimport matplotlib.pyplot as plt sns.set_theme(style="whitegrid")df = sns.load_dataset("tips")#默認畫條形圖sns.barplot(x="day",y="total_bill",data=df,ci="sd",capsize=1.0)## 注意這個ci參數plt.show()print(df.groupby("day").agg({"total_bill":[np.mean]}))print(df.groupby("day").agg({"total_bill":[np.std]}))

     total_bill           meandayThur  17.682742Fri   17.151579Sat   20.441379Sun   21.410000     total_bill            stddayThur   7.886170Fri    8.302660Sat    9.480419Sun    8.832122

設置置信區間(68)

import seaborn as snsimport matplotlib.pyplot as plt sns.set_theme(style="whitegrid")df = sns.load_dataset("tips")#默認畫條形圖sns.barplot(x="day",y="total_bill",data=df,ci=68,capsize=1.0)## 注意這個ci參數plt.show()

設置置信區間(95)

import seaborn as snsimport matplotlib.pyplot as plt sns.set_theme(style="whitegrid")df = sns.load_dataset("tips")#默認畫條形圖sns.barplot(x="day",y="total_bill",data=df,ci=95)plt.show()#計算平均值看是否和條形圖得高度一致print(df.groupby("day").agg({"total_bill":[np.mean]}))

     total_bill           meandayThur  17.682742Fri   17.151579Sat   20.441379Sun   21.410000

dataframe aggregate函數使用

#計算平均值看是否和條形圖得高度一致df = sns.load_dataset("tips")print("="*20)print(df.groupby("day").agg({"total_bill":[np.mean]})) # 分組求均值print("="*20)print(df.groupby("day").agg({"total_bill":[np.std]})) # 分組求標準差print("="*20)print(df.groupby("day").agg({"total_bill":"nunique"})) # 這里統計得是不同得數目print("="*20)print(df.groupby("day").agg({"total_bill":"count"})) # 這里統計得是每個分組樣本得數量print("="*20)print(df["day"].value_counts())print("="*20)

====================     total_bill           meandayThur  17.682742Fri   17.151579Sat   20.441379Sun   21.410000====================     total_bill            stddayThur   7.886170Fri    8.302660Sat    9.480419Sun    8.832122====================      total_billdayThur          61Fri           18Sat           85Sun           76====================      total_billdayThur          62Fri           19Sat           87Sun           76====================Sat     87Sun     76Thur    62Fri     19Name: day, dtype: int64====================

dataframe aggregate 自定義函數

import numpy as npimport pandas as pddf = pd.DataFrame({'Buy/Sell': [1, 0, 1, 1, 0, 1, 0, 0],                   'Trader': ['A', 'A', 'B', 'B', 'B', 'C', 'C', 'C']})print(df)def categorize(x):    m = x.mean()    return 1 if m > 0.5 else 0 if m < 0.5 else np.nanresult = df.groupby(['Trader'])['Buy/Sell'].agg([categorize, 'sum', 'count'])result = result.rename(columns={'categorize' : 'Buy/Sell'})result

   Buy/Sell Trader0         1      A1         0      A2         1      B3         1      B4         0      B5         1      C6         0      C7         0      C

dataframe aggregate 自定義函數2

df = sns.load_dataset("tips")#默認畫條形圖def custom1(x):    m = x.mean()    s = x.std()    n = x.count()# 統計個數    #print(n)    return m+1.96*s/np.sqrt(n)def custom2(x):    m = x.mean()    s = x.std()    n = x.count()# 統計個數    #print(n)    return m+s/np.sqrt(n)sns.barplot(x="day",y="total_bill",data=df,ci=95)plt.show()print(df.groupby("day").agg({"total_bill":[np.std,custom1]})) # 分組求標準差sns.barplot(x="day",y="total_bill",data=df,ci=68)plt.show()print(df.groupby("day").agg({"total_bill":[np.std,custom2]})) #

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     total_bill            std    custom1dayThur   7.886170  19.645769Fri    8.302660  20.884910Sat    9.480419  22.433538Sun    8.832122  23.395703

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     total_bill            std    custom2dayThur   7.886170  18.684287Fri    8.302660  19.056340Sat    9.480419  21.457787Sun    8.832122  22.423114

seaborn顯示網格

ax=sns.barplot(x="day",y="total_bill",data=df,ci=95)ax.yaxis.grid(True) # Hide the horizontal gridlinesax.xaxis.grid(True) # Show the vertical gridlines

seaborn設置刻度

fig, ax = plt.subplots(figsize=(10, 8))sns.barplot(x="day",y="total_bill",data=df,ci=95,ax=ax)ax.set_yticks([i for i in range(30)])ax.yaxis.grid(True) # Hide the horizontal gridlines

使用其他estaimator

#estimator 指定條形圖高度使用相加得和sns.barplot(x="day",y="total_bill",data=df,estimator=np.sum)plt.show()#計算想加和看是否和條形圖得高度一致print(df.groupby("day").agg({"total_bill":[np.sum]}))'''     total_bill            sumdayFri      325.88Sat     1778.40Sun     1627.16Thur    1096.33'''

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